US20200322411A1 - Bandwidth usage reduction employing media treading with reductive edging - Google Patents
Bandwidth usage reduction employing media treading with reductive edging Download PDFInfo
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- US20200322411A1 US20200322411A1 US16/376,346 US201916376346A US2020322411A1 US 20200322411 A1 US20200322411 A1 US 20200322411A1 US 201916376346 A US201916376346 A US 201916376346A US 2020322411 A1 US2020322411 A1 US 2020322411A1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
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- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
- H04N21/26258—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists for generating a list of items to be played back in a given order, e.g. playlist, or scheduling item distribution according to such list
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- H04L65/61—Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio
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Definitions
- aspects of the present disclosure generally relate to streaming media and more particularly to reduction of bandwidth usage in streaming media using reductive edging techniques.
- a user's watch habits often reflect that they do not fully watch an episode, show, program or content in one continuous session and jump to the next one.
- players reflect a greedy behavior where they download all available media segments of an asset that is being played.
- a TCP protocol would fully download a progressive mp 4 asset on a browser environment on a laptop device even if the video is paused.
- This inherent greedy behavior of the streaming media devices is to ensure best playback experience on the user device and reduce network errors.
- these algorithms do not consider the user's watch habits when downloading media segments. More often, a user does not fully watch the program and the content downloaded ends up as simply being a waste of network and provider bandwidth.
- FIG. 1 is a block diagram showing the network lay of a reductive edging system according to aspects of the present disclosure.
- FIG. 2 is flow diagram that depicts the method for bandwidth usage reduction in media streaming according to aspects of the present disclosure.
- FIG. 3 is a block system diagram that shows a standalone Reductive Edging device according to aspects of the present disclosure
- FIG. 4 is a block system diagram that depicts an embedded Reductive Edging system according to aspects of the present disclosure
- aspects of the present disclosure involve prospectively requesting media segments based on a user model before receiving a request for the media segments.
- the user model is based on user streaming information that reflects a user's media consumption habits.
- the probability of a user abandoning a video at the onset is much higher and decreases as the user spends more time watching the content.
- the time spent by a user watching streaming content is therefore a good indicator of engagement and may be a valid metric of user behavior with respect to consumption of media.
- the type of video can also play a role in this.
- the model may also take into account historical information regarding how much time a user spends consuming media content (e.g., watching videos or listening to audio or reading text) before switching to other content. Alternatively, the model may take into account historical information regarding what fraction of an item of content the user tends to consume.
- FIG. 1 is a block diagram showing the network lay of a reductive edging system according to aspects of the present disclosure.
- a remote reductive edging device 102 may be a connected to a network 105 and communicatively coupled to a client device 104 and a Content Delivery Network Server (CDN) 101 through the network 105 .
- the client device 104 is directly connected to an OTT device 103 through for example and without limitation, an Ethernet cable, Universal Serial Bus (USB) cable, or similar.
- the directly connected OTT device 103 may be a reductive edging device communicative coupled to the CDN 101 through the network 105 .
- the OTT device 103 is also communicatively coupled to the remote reductive edging device 102 .
- a reductive edging device having a predictive user model that decreases the bandwidth usage during streaming and while prospectively caching media segments.
- a reductive edger works across all client devices to improve streaming media start times.
- the reductive edger generally improves streaming media times by performing network handshaking operations and storing network information before a client device requires these operations.
- the reductive edger may also employ prospective caching of media segments before the media segments are requested by the client device.
- FIG. 2 depicts the method for bandwidth usage reduction in media streaming according to aspects of the present disclosure.
- the reductive edger device having a predictive user model may prospectively download playlists and media segments as discussed above. Playlists and media segments are sent to the client device upon request 201 . While streaming media segments the Reductive Edging device (RE) may monitor the streaming progress of the client device 202 using user streaming information received from the client device.
- RE Reductive Edging device
- the RE may monitor requests for segments sent to RE from the client device.
- the RE may compare the length of a media segment to the length of time between the time the media segment was sent and the next media segment request is received to determine the user's behavior.
- a time length between sending the media segment and receiving a request for another media segment may indicate that the user has paused or stopped the stream.
- the RE may also monitor the media information requested by the client device to determine when the user has stopped streaming one media title and begun streaming another different media title. For example and without limitation after sending a media segment to a client device a request for a media playlist for a different media title indicates that the user has stopped streaming a first media title.
- the client device may communicate to the RE the position of the play head of the media player during playback of the media segments.
- the RE may be made aware of all video related requests from a client. Monitoring connotes reading or even capturing information. The key here is what to infer or learn from the captured information.
- the RE may take the user streaming information and store it 203 .
- the user streaming information is then used to refine a user model 204 .
- One of the RE's capabilities is to remediate a client's shortcomings.
- a client might be very greedy and buffer an unnecessarily large amount of video data that is ultimately wasted. In such situations, the may temper a client's requests knowing, for example, a client already received and buffered a significant amount of data and additional acquisition is likely to be wasteful. By moderating excessive requests, waste may be minimized without risking a user's experience.
- the RE may have a user model that describes the user's streaming habits.
- This user model may include by way of example and not by way of limitation a series of thresholds describing the amount of media segments to be downloaded and held in the cache at time points during streaming. These thresholds may represent the average length of time the user streams a media title based on the current position of the media player in a media title.
- user streaming information such as the length of time the media title has been streaming and point at which the user has stopped streaming may be averaged with the user model at the relevant thresholds to refine the user model.
- Each user model may be customized based on: client device, user biographical information, or user identifier such as a user account or user identification number.
- the model may be customized based on factors such as a user's viewing habits (e.g., scrolling, trick playback, etc.), content, genre, time of day, week, month, year, current events (natural disaster, elections, etc.)
- the model may be customized based on sporting event schedules, e.g., NFL, MLB etc. For example, suppose a user regularly watches a DVR of Formula One racing every Saturday morning at approximately the same time. When the Formula One racing season starts the model could predict that the user is likely to watch this content from start to finish, as opposed to on a Wednesday where the user is more likely to Channel/Content Surf.
- the user model also accounts for media genre and each threshold in the user model may be different depending on the genre of the media title.
- the user model is a Neural Network trained to predict the amount of media segments to be downloaded and cached based on the length of time a media title has been watched.
- the user streaming information may be provided to the neural network and a machine-learning algorithm may be used to further train the neural network with user streaming information. All of the previously mentioned factors can be input to the neural net model.
- the neural model may be modest, e.g., a shallow learning model, since it is only about a given user and the ‘training’ process can work in the background.
- the client device may initiate streaming on another media title 205 .
- the RE uses the refined user model to cache media segments before receiving a request for the media segments from the client device 206 .
- the RE Upon receiving a request for a media segment, the RE sends the media segment to the client device 207 .
- additional media segments may be downloaded before being requested by the client device based on the refined user model.
- FIG. 3 shows a standalone Reductive Edging device according to aspects of the present disclosure.
- the standalone Reductive Edging device or Edger 300 may be coupled to a local OTT device or client device 302 through a network interface 307 over a LAN or WAN.
- the standalone Reductive Edging device may be in communication through the network interface 307 with a non-local device 303 e.g., servers or another client, through a large network 304 such as the internet.
- the client device is connected to the stand alone Reductive Edging device through a communication bus (not shown) such as, without limitation, a peripheral interconnect (PCI) bus, PCI express bus, Universal Serial Bus (USB), Ethernet port, Fire-wire connector or similar interface.
- PCI peripheral interconnect
- USB Universal Serial Bus
- the standalone Reductive Edging device 300 may include one or more processor units 306 , which may be configured according to well-known architectures, such as, e.g., single-core, dual-core, quad-core, multi-core, processor-coprocessor, cell processor, and the like.
- the standalone Reductive Edging device 300 may also include one or more memory units 305 (e.g., random access memory (RAM), dynamic random access memory (DRAM), read-only memory (ROM), and the like).
- RAM random access memory
- DRAM dynamic random access memory
- ROM read-only memory
- the processor unit 306 may execute one or more instructions 308 , portions of which may be stored in the memory 305 and the processor 306 may be operatively coupled to the memory through a bus or bus type connection.
- the instructions 308 may be configured to implement the method for bandwidth usage reduction in streaming systems shown in FIG. 2 as well as instructions for refining a user model.
- the Memory 305 may contain instructions for storing Playlists and Link locations and a Protocol Stack defining HLS server locations.
- the Memory 305 may also contain the HLS Library 310 , user streaming data 314 , a Protocol Stack 311 , and a user model 312 .
- the user model 312 could be static for simplicity or dynamic like a shallow learning neural net, as discussed above. Once a user model has been formed, one merely supplies the input and the model can produce guidance on whether to temper user requests to better manage bandwidth usage.
- the term “protocol stack” or network stack refers to an implementation of a computer networking protocol suite or protocol family.
- a protocol suite is a definition of a communication protocol
- a protocol stack is the software implementation of the protocol suite.
- Individual protocols within a suite are often designed as software modules, each having a single purpose in mind to facilitate design and evaluation. Because each protocol module usually communicates with two others, they are commonly imagined as layers in a stack of protocols. The lowest level protocol deals with low-level interaction with the communications hardware. Higher layers add more features and capability. User applications usually deal only with the topmost layers.
- the protocol stack 311 may include the following protocols at the following layers: Hyper Text Transfer Protocol (HTTP) at the Application layer; Transfer Control Protocol (TCP) at the Transport layer; Internet Protocol (IP) at the Internet/Network Layer; Ethernet at the Data Link/Link layer; and IEEE 802.3u at the Physical layer.
- HTTP Hyper Text Transfer Protocol
- TCP Transfer Control Protocol
- IP Internet Protocol
- Ethernet Ethernet at the Data Link/Link layer
- IEEE 802.3u at the Physical layer.
- the instructions 308 may further implement analyzing link locations within web resources and storing the URL of the links and location of the links within the converted video frame 314 .
- the Cache 309 may also be located in memory 305 .
- the standalone Reductive Edging device 300 may include a network interface 307 to facilitate communication via an electronic communications network 304 .
- the network interface 307 may be configured to implement wired or wireless communication over local area networks and wide area networks such as the Internet.
- the device 300 may send and receive data and/or requests for files via one or more message packets over the network 304 .
- Message packets sent over the network 304 may temporarily be stored in a cache 309 in memory 305 .
- the client device 302 may connect through the network interface 307 to the electronic communications network 304 .
- the client device 303 may be in communication with the standalone Reductive Edging device 300 over the electronic communication network 304 .
- FIG. 4 depicts an embedded Reductive Edging system 400 according to aspects of the present disclosure.
- the Reductive Edging System 400 may be embedded anywhere that can access both content and the client device.
- the embedded Reductive Edging system may be embedded in a CDN server, an OTT device, e.g., a television, coupled to a user's input device 402 .
- the user's input device 402 may be a controller, touch screen, microphone, keyboard, mouse, joystick or other device that allows the user to input information including sound data in to the system.
- the computing device of the embedded Reductive Edging system 400 may include one or more processor units 403 , which may be configured according to well-known architectures, such as, e.g., single-core, dual-core, quad-core, multi-core, processor-coprocessor, cell processor, and the like.
- the computing device may also include one or more memory units 404 (e.g., random access memory (RAM), dynamic random access memory (DRAM), read-only memory (ROM), and the like).
- RAM random access memory
- DRAM dynamic random access memory
- ROM read-only memory
- the processor unit 403 may execute one or more programs, portions of which may be stored in the memory 404 and the processor 403 may be operatively coupled to the memory, e.g., by accessing the memory via a data bus 405 .
- the programs may be configured to implement streaming media through HLS systems 408 .
- the Memory 404 may contain information about connections between the system and one or more streaming servers 410 .
- the Memory 404 may also contain a buffer of media segments 409 .
- the Media segments and connection information may also be stored as data 418 in the Mass Store 418 .
- the computing device 400 may also include well-known support circuits, such as input/output (I/O) 407 , circuits, power supplies (P/S) 411 , a clock (CLK) 412 , and cache 413 , which may communicate with other components of the system, e.g., via the bus 405 .
- the computing device may include a network interface 414 .
- the processor unit 403 and network interface 414 may be configured to implement a local area network (LAN) or personal area network (PAN), via a suitable network protocol, e.g., Bluetooth, for a PAN.
- LAN local area network
- PAN personal area network
- the computing device may optionally include a mass storage device 415 such as a disk drive, CD-ROM drive, tape drive, flash memory, or the like, and the mass storage device may store programs and/or data.
- the computing device may also include a user interface 416 to facilitate interaction between the system and a user.
- the user interface may include a monitor, Television screen, speakers, headphones or other devices that communicate information to the user.
- the computing device 400 may include a network interface 414 to facilitate communication via an electronic communications network 420 .
- the network interface 414 may be configured to implement wired or wireless communication over local area networks and wide area networks such as the Internet.
- the device 400 may send and receive data and/or requests for files via one or more message packets over the network 420 .
- Message packets sent over the network 420 may temporarily be stored in a buffer 409 in memory 404 .
- the embedded Reductive Edging or embedded Edger 421 may be an embedded hardware component of a CDN server 400 , which may be coupled to the main processor via the bus and requests may be received from applications, e.g., streaming applications, running on the client device.
- the embedded Edger 421 may initiate and intercept network communications directed toward a CDN or other servers.
- the embedded Edger 421 may lack a network interface or the network interface may not be used.
- the embedded Edger, the functions of the edger may be implemented in streaming software 408 stored in the memory 404 or in programs 417 stored in the mass store 415 and executed on the processor 403 .
- the embedded Edger 421 may be an external device coupled to the client device 400 , e.g., via a local non-network connection, such as the I/O functions 407 .
- the processor of the embedded Edger unit 421 may execute one or more instructions 424 , portions of which may be stored in the edger memory 422 and the processor 423 may be operatively coupled to the memory 422 through a bus or bus type connection.
- the instructions 424 may be configured to implement the method for implementing interactive video in non-browser based streaming systems shown in FIG. 2 .
- the Memory 422 may contain instructions for storing Playlists and a Protocol Stack defining HLS server locations.
- the Memory 422 may also contain the HLS Library 310 , the Protocol Stack 311 , user streaming data 314 and a user model 312 .
- the instructions 324 may further implement storage of media segments as data 325 during operation.
- the instructions 424 may further implement using user information for refinement of a user model 314 .
- the HLS Library, Protocol stack and media segments may be stored on the client device 400 in the buffer 408 or as connection information 408 in memory 404 or as data 418 in the Mass Store 415 .
- the components, process steps, and/or data structures may be implemented using various types of operating systems; computing platforms; user interfaces/displays, including personal or laptop computers, video game consoles, PDAs and other handheld devices, such as cellular telephones, tablet computers, portable gaming devices; and/or general purpose machines.
- computing platforms including personal or laptop computers, video game consoles, PDAs and other handheld devices, such as cellular telephones, tablet computers, portable gaming devices; and/or general purpose machines.
- devices of a less general purpose nature such as hardwired devices, field programmable gate arrays (FOGs), application specific integrated circuits (ASICs), or the like, may also be used without departing from the scope and spirit of the inventive concepts disclosed herein.
- FOGs field programmable gate arrays
- ASICs application specific integrated circuits
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Abstract
Description
- Aspects of the present disclosure generally relate to streaming media and more particularly to reduction of bandwidth usage in streaming media using reductive edging techniques.
- A user's watch habits often reflect that they do not fully watch an episode, show, program or content in one continuous session and jump to the next one. Depending on the encoding standard and/or the buffer available on the client devices, players reflect a greedy behavior where they download all available media segments of an asset that is being played.
- For example, a TCP protocol would fully download a progressive mp4 asset on a browser environment on a laptop device even if the video is paused. This inherent greedy behavior of the streaming media devices is to ensure best playback experience on the user device and reduce network errors. However, these algorithms do not consider the user's watch habits when downloading media segments. More often, a user does not fully watch the program and the content downloaded ends up as simply being a waste of network and provider bandwidth.
- It is within this context that aspects of the present disclosure arise.
- Objects and advantages of aspects of the present disclosure will become apparent upon reading, the following detailed description and upon reference to the accompanying drawings in which:
-
FIG. 1 is a block diagram showing the network lay of a reductive edging system according to aspects of the present disclosure. -
FIG. 2 is flow diagram that depicts the method for bandwidth usage reduction in media streaming according to aspects of the present disclosure. -
FIG. 3 is a block system diagram that shows a standalone Reductive Edging device according to aspects of the present disclosure -
FIG. 4 is a block system diagram that depicts an embedded Reductive Edging system according to aspects of the present disclosure - Although the following detailed description contains many specific details for the purposes of illustration, anyone of ordinary skill in the art will appreciate that many variations and alterations to the following details are within the scope of the invention. Accordingly, the exemplary embodiments of the invention described below are set forth without any loss of generality to, and without imposing limitations upon, the claimed invention.
- Introduction
- Aspects of the present disclosure involve prospectively requesting media segments based on a user model before receiving a request for the media segments. The user model is based on user streaming information that reflects a user's media consumption habits. By way of example, and not by way of limitation, the probability of a user abandoning a video at the onset is much higher and decreases as the user spends more time watching the content. The time spent by a user watching streaming content is therefore a good indicator of engagement and may be a valid metric of user behavior with respect to consumption of media. In some implementations, the type of video can also play a role in this. For example if historical data shows that the user binge watches a certain television series this information can be used as a metric in the algorithm when playing that show as opposed to a new show the user has never seen before. The model may also take into account historical information regarding how much time a user spends consuming media content (e.g., watching videos or listening to audio or reading text) before switching to other content. Alternatively, the model may take into account historical information regarding what fraction of an item of content the user tends to consume.
-
FIG. 1 is a block diagram showing the network lay of a reductive edging system according to aspects of the present disclosure. A remotereductive edging device 102 may be a connected to anetwork 105 and communicatively coupled to aclient device 104 and a Content Delivery Network Server (CDN) 101 through thenetwork 105. In other embodiments, theclient device 104 is directly connected to anOTT device 103 through for example and without limitation, an Ethernet cable, Universal Serial Bus (USB) cable, or similar. In some embodiments, the directly connectedOTT device 103 may be a reductive edging device communicative coupled to theCDN 101 through thenetwork 105. In other embodiments, theOTT device 103 is also communicatively coupled to the remotereductive edging device 102. - According to aspects of the present disclosure, a reductive edging device having a predictive user model that decreases the bandwidth usage during streaming and while prospectively caching media segments. A reductive edger works across all client devices to improve streaming media start times. The reductive edger generally improves streaming media times by performing network handshaking operations and storing network information before a client device requires these operations. The reductive edger may also employ prospective caching of media segments before the media segments are requested by the client device. These operations are described in Co-pending application No. 16/191,341 the contents of which are incorporated by reference herein for all purposes.
- Method for Bandwidth Usage Reduction
-
FIG. 2 depicts the method for bandwidth usage reduction in media streaming according to aspects of the present disclosure. Initially the reductive edger device having a predictive user model may prospectively download playlists and media segments as discussed above. Playlists and media segments are sent to the client device uponrequest 201. While streaming media segments the Reductive Edging device (RE) may monitor the streaming progress of theclient device 202 using user streaming information received from the client device. - By way of example and not by way of limitation the RE may monitor requests for segments sent to RE from the client device. The RE may compare the length of a media segment to the length of time between the time the media segment was sent and the next media segment request is received to determine the user's behavior. By way of example, and not by way of limitation, a time length between sending the media segment and receiving a request for another media segment may indicate that the user has paused or stopped the stream. The RE may also monitor the media information requested by the client device to determine when the user has stopped streaming one media title and begun streaming another different media title. For example and without limitation after sending a media segment to a client device a request for a media playlist for a different media title indicates that the user has stopped streaming a first media title. In some embodiments of the present disclosure, the client device may communicate to the RE the position of the play head of the media player during playback of the media segments. The RE may be made aware of all video related requests from a client. Monitoring connotes reading or even capturing information. The key here is what to infer or learn from the captured information.
- The RE may take the user streaming information and store it 203. The user streaming information is then used to refine a
user model 204. One of the RE's capabilities is to remediate a client's shortcomings. A client might be very greedy and buffer an unnecessarily large amount of video data that is ultimately wasted. In such situations, the may temper a client's requests knowing, for example, a client already received and buffered a significant amount of data and additional acquisition is likely to be wasteful. By moderating excessive requests, waste may be minimized without risking a user's experience. - By way of example and not by way of limitation the RE may have a user model that describes the user's streaming habits. This user model may include by way of example and not by way of limitation a series of thresholds describing the amount of media segments to be downloaded and held in the cache at time points during streaming. These thresholds may represent the average length of time the user streams a media title based on the current position of the media player in a media title. In this case user streaming information such as the length of time the media title has been streaming and point at which the user has stopped streaming may be averaged with the user model at the relevant thresholds to refine the user model. Each user model may be customized based on: client device, user biographical information, or user identifier such as a user account or user identification number. In addition to the static demographic information, the model may be customized based on factors such as a user's viewing habits (e.g., scrolling, trick playback, etc.), content, genre, time of day, week, month, year, current events (natural disaster, elections, etc.) In some implementations the model may be customized based on sporting event schedules, e.g., NFL, MLB etc. For example, suppose a user regularly watches a DVR of Formula One racing every Saturday morning at approximately the same time. When the Formula One racing season starts the model could predict that the user is likely to watch this content from start to finish, as opposed to on a Wednesday where the user is more likely to Channel/Content Surf.
- In some embodiments, the user model also accounts for media genre and each threshold in the user model may be different depending on the genre of the media title. In alternative embodiments, the user model is a Neural Network trained to predict the amount of media segments to be downloaded and cached based on the length of time a media title has been watched. The user streaming information may be provided to the neural network and a machine-learning algorithm may be used to further train the neural network with user streaming information. All of the previously mentioned factors can be input to the neural net model. The neural model may be modest, e.g., a shallow learning model, since it is only about a given user and the ‘training’ process can work in the background.
- Subsequently, the client device may initiate streaming on another
media title 205. The RE uses the refined user model to cache media segments before receiving a request for the media segments from theclient device 206. Upon receiving a request for a media segment, the RE sends the media segment to theclient device 207. As streaming continues, additional media segments may be downloaded before being requested by the client device based on the refined user model. - System for Bandwidth Usage Reduction
-
FIG. 3 shows a standalone Reductive Edging device according to aspects of the present disclosure. The standalone Reductive Edging device orEdger 300 may be coupled to a local OTT device orclient device 302 through anetwork interface 307 over a LAN or WAN. In other alternative implementation the standalone Reductive Edging device may be in communication through thenetwork interface 307 with anon-local device 303 e.g., servers or another client, through alarge network 304 such as the internet. In some implementations the client device is connected to the stand alone Reductive Edging device through a communication bus (not shown) such as, without limitation, a peripheral interconnect (PCI) bus, PCI express bus, Universal Serial Bus (USB), Ethernet port, Fire-wire connector or similar interface. - The standalone
Reductive Edging device 300 may include one ormore processor units 306, which may be configured according to well-known architectures, such as, e.g., single-core, dual-core, quad-core, multi-core, processor-coprocessor, cell processor, and the like. The standaloneReductive Edging device 300 may also include one or more memory units 305 (e.g., random access memory (RAM), dynamic random access memory (DRAM), read-only memory (ROM), and the like). - The
processor unit 306 may execute one ormore instructions 308, portions of which may be stored in thememory 305 and theprocessor 306 may be operatively coupled to the memory through a bus or bus type connection. Theinstructions 308 may be configured to implement the method for bandwidth usage reduction in streaming systems shown inFIG. 2 as well as instructions for refining a user model. Additionally theMemory 305 may contain instructions for storing Playlists and Link locations and a Protocol Stack defining HLS server locations. - The
Memory 305 may also contain theHLS Library 310,user streaming data 314, aProtocol Stack 311, and auser model 312. Theuser model 312 could be static for simplicity or dynamic like a shallow learning neural net, as discussed above. Once a user model has been formed, one merely supplies the input and the model can produce guidance on whether to temper user requests to better manage bandwidth usage. As used, herein, the term “protocol stack” or network stack refers to an implementation of a computer networking protocol suite or protocol family. In general terms, a protocol suite is a definition of a communication protocol, and a protocol stack is the software implementation of the protocol suite. Individual protocols within a suite are often designed as software modules, each having a single purpose in mind to facilitate design and evaluation. Because each protocol module usually communicates with two others, they are commonly imagined as layers in a stack of protocols. The lowest level protocol deals with low-level interaction with the communications hardware. Higher layers add more features and capability. User applications usually deal only with the topmost layers. - By way of example, and not by way of limitation, the
protocol stack 311 may include the following protocols at the following layers: Hyper Text Transfer Protocol (HTTP) at the Application layer; Transfer Control Protocol (TCP) at the Transport layer; Internet Protocol (IP) at the Internet/Network Layer; Ethernet at the Data Link/Link layer; and IEEE 802.3u at the Physical layer. - The
instructions 308 may further implement analyzing link locations within web resources and storing the URL of the links and location of the links within the convertedvideo frame 314. TheCache 309 may also be located inmemory 305. - The standalone
Reductive Edging device 300 may include anetwork interface 307 to facilitate communication via anelectronic communications network 304. Thenetwork interface 307 may be configured to implement wired or wireless communication over local area networks and wide area networks such as the Internet. Thedevice 300 may send and receive data and/or requests for files via one or more message packets over thenetwork 304. Message packets sent over thenetwork 304 may temporarily be stored in acache 309 inmemory 305. Theclient device 302 may connect through thenetwork interface 307 to theelectronic communications network 304. Alternatively, theclient device 303 may be in communication with the standaloneReductive Edging device 300 over theelectronic communication network 304. -
FIG. 4 depicts an embeddedReductive Edging system 400 according to aspects of the present disclosure. In general, theReductive Edging System 400 may be embedded anywhere that can access both content and the client device. By way of example, and not by way of limitation, the embedded Reductive Edging system may be embedded in a CDN server, an OTT device, e.g., a television, coupled to a user'sinput device 402. The user'sinput device 402 may be a controller, touch screen, microphone, keyboard, mouse, joystick or other device that allows the user to input information including sound data in to the system. - The computing device of the embedded
Reductive Edging system 400 may include one ormore processor units 403, which may be configured according to well-known architectures, such as, e.g., single-core, dual-core, quad-core, multi-core, processor-coprocessor, cell processor, and the like. The computing device may also include one or more memory units 404 (e.g., random access memory (RAM), dynamic random access memory (DRAM), read-only memory (ROM), and the like). - The
processor unit 403 may execute one or more programs, portions of which may be stored in thememory 404 and theprocessor 403 may be operatively coupled to the memory, e.g., by accessing the memory via adata bus 405. The programs may be configured to implement streaming media throughHLS systems 408. Additionally theMemory 404 may contain information about connections between the system and one ormore streaming servers 410. TheMemory 404 may also contain a buffer ofmedia segments 409. The Media segments and connection information may also be stored as data 418 in the Mass Store 418. - The
computing device 400 may also include well-known support circuits, such as input/output (I/O) 407, circuits, power supplies (P/S) 411, a clock (CLK) 412, andcache 413, which may communicate with other components of the system, e.g., via thebus 405. The computing device may include anetwork interface 414. Theprocessor unit 403 andnetwork interface 414 may be configured to implement a local area network (LAN) or personal area network (PAN), via a suitable network protocol, e.g., Bluetooth, for a PAN. The computing device may optionally include amass storage device 415 such as a disk drive, CD-ROM drive, tape drive, flash memory, or the like, and the mass storage device may store programs and/or data. The computing device may also include auser interface 416 to facilitate interaction between the system and a user. The user interface may include a monitor, Television screen, speakers, headphones or other devices that communicate information to the user. - The
computing device 400 may include anetwork interface 414 to facilitate communication via anelectronic communications network 420. Thenetwork interface 414 may be configured to implement wired or wireless communication over local area networks and wide area networks such as the Internet. Thedevice 400 may send and receive data and/or requests for files via one or more message packets over thenetwork 420. Message packets sent over thenetwork 420 may temporarily be stored in abuffer 409 inmemory 404. - In some implementations, the embedded Reductive Edging or embedded Edger 421 may be an embedded hardware component of a
CDN server 400, which may be coupled to the main processor via the bus and requests may be received from applications, e.g., streaming applications, running on the client device. In some implementations, the embedded Edger 421 may initiate and intercept network communications directed toward a CDN or other servers. In these implementations, the embedded Edger 421 may lack a network interface or the network interface may not be used. In other implementations, the embedded Edger, the functions of the edger may be implemented instreaming software 408 stored in thememory 404 or in programs 417 stored in themass store 415 and executed on theprocessor 403. - In some alternative implementation the embedded Edger 421 may be an external device coupled to the
client device 400, e.g., via a local non-network connection, such as the I/O functions 407. - The processor of the embedded Edger unit 421 may execute one or
more instructions 424, portions of which may be stored in theedger memory 422 and theprocessor 423 may be operatively coupled to thememory 422 through a bus or bus type connection. Theinstructions 424 may be configured to implement the method for implementing interactive video in non-browser based streaming systems shown inFIG. 2 . Additionally theMemory 422 may contain instructions for storing Playlists and a Protocol Stack defining HLS server locations. TheMemory 422 may also contain theHLS Library 310, theProtocol Stack 311,user streaming data 314 and auser model 312. The instructions 324 may further implement storage of media segments as data 325 during operation. Theinstructions 424 may further implement using user information for refinement of auser model 314. Alternatively the HLS Library, Protocol stack and media segments may be stored on theclient device 400 in thebuffer 408 or asconnection information 408 inmemory 404 or as data 418 in theMass Store 415. - In the interest of clarity, not all of the routine features of the implementations described herein are shown and described. It will be understood by those skilled in the art that in the development of any such implementations, numerous implementation-specific decisions must be made in order to achieve the developer's specific goals, such as compliance with application- and business-related constraints, and that these specific goals will vary from one implementation to another and from one developer to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking of engineering for those of ordinary skill in the art having the benefit of the present disclosure.
- In accordance with aspects of the present disclosure, the components, process steps, and/or data structures may be implemented using various types of operating systems; computing platforms; user interfaces/displays, including personal or laptop computers, video game consoles, PDAs and other handheld devices, such as cellular telephones, tablet computers, portable gaming devices; and/or general purpose machines. In addition, those of ordinary skill in the art will recognize that devices of a less general purpose nature, such as hardwired devices, field programmable gate arrays (FOGs), application specific integrated circuits (ASICs), or the like, may also be used without departing from the scope and spirit of the inventive concepts disclosed herein.
- While the above is a complete description of the preferred embodiment of the present invention, it is possible to use various alternatives, modifications and equivalents. Therefore, the scope of the present invention should be determined not with reference to the above description but should, instead, be determined with reference to the appended claims, along with their full scope of equivalents. Any feature, whether preferred or not, may be combined with any other feature, whether preferred or not. In the claims that follow, the indefinite article “A”, or “An” refers to a quantity of one or more of the item following the article, except where expressly stated otherwise. The appended claims are not to be interpreted as including means-plus-function limitations, unless such a limitation is explicitly recited in a given claim using the phrase “means for.”
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US6947935B1 (en) * | 2001-04-04 | 2005-09-20 | Microsoft Corporation | Training, inference and user interface for guiding the caching of media content on local stores |
CA2677145C (en) * | 2007-02-01 | 2016-08-16 | Invidi Technologies Corporation | Verifying and encouraging asset consumption in a communications network |
US20170104840A1 (en) * | 2015-10-08 | 2017-04-13 | Perry M. Spagnola | Adaptive system cache |
US10582227B2 (en) * | 2016-01-15 | 2020-03-03 | Vid Scale, Inc. | Scalable coding based video distribution |
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US10979778B2 (en) * | 2017-02-01 | 2021-04-13 | Rovi Guides, Inc. | Systems and methods for selecting type of secondary content to present to a specific subset of viewers of a media asset |
US10158891B1 (en) * | 2017-06-05 | 2018-12-18 | Verizon Digital Media Services Inc. | Prefetcher with adaptive stream segment prefetch window based on client associated thresholds |
US20180365270A1 (en) * | 2017-06-19 | 2018-12-20 | Get Attached, Inc. | Context aware digital media browsing |
US10819645B2 (en) * | 2017-09-20 | 2020-10-27 | Futurewei Technologies, Inc. | Combined method for data rate and field of view size adaptation for virtual reality and 360 degree video streaming |
US11036357B2 (en) * | 2017-09-27 | 2021-06-15 | Salesforce.Com, Inc. | Generating or updating cross-community streams |
US11196669B2 (en) * | 2018-05-17 | 2021-12-07 | At&T Intellectual Property I, L.P. | Network routing of media streams based upon semantic contents |
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